Bacterial salivary microbiome associates with asthma among african american children and young adults

Several studies have shown that the airways of asthma patients contain higher diversity of bacteria and are enriched in pathogenic species. However, sampling the airways in children is challenging. Here we aimed to identify differences in the salivary bacterial composition between African Americans children with and without asthma. Saliva samples from 57 asthma cases and 57 healthy controls were analyzed by means of 16S ribosomal RNA amplicon profiling. Measurements of bacterial diversity and genus relative abundance were compared between cases and controls using the nonparametric Wilcoxon test and multivariate regression models. A total of five phyla and a mean of 56 genera were identified. Among them, 15 genera had a relative abundance greater than 1%, being Prevotella, Haemophilus, Streptococcus, and Veillonella the most abundant genera. Differences between cases and controls were found in terms of diversity, as well as in relative abundance for Streptococcus genus (13.0% in cases vs 18.3% in controls; P = .003) and Veillonella genus (11.1% in cases vs 8.0% in controls; P = .002). These differences remained significant after correction for multiple comparisons and when potential confounders were taken into account in logistic regression models. In conclusion, we identified changes in the salivary microbiota associated with asthma among African Americans.     

Acute Care Diagnostic Collaboration: Bayesian modeling comparative diagnostic assessment of lactate,procalcitonin and CRP in risk stratified population by Mortality in ED (MEDS) score

Objective

To assess and compare the diagnostic value of lactate, procalcitonin (PCT) and C-reactive protein (CRP) in low, moderate, and high-risk stratified population applying Mortality in Emergency Department (MEDS) risk score using Bayesian statistical modeling.

Incremental diagnostic quality gain of CTA over V/Q scan in the assessment of pulmonary embolism by means of a Wells score Bayesian model: results from the ACDC collaboration

Objective

Our objective was to evaluate the diagnostic value of computed tomography angiography (CTA) and ventilation perfusion (V/Q) scan in the assessment of pulmonary embolism (PE) by means of a Bayesian statistical model.

Methods

Wells criteria defined pretest probability. Sensitivity and specificity of CTA and V/Q scan for PE were derived from pooled meta-analysis data. Likelihood ratios calculated for CTA and V/Q were inserted in the nomogram. Absolute (ADG) and relative diagnostic gains (RDG) were analyzed comparing post- and pretest probability. Comparative gain difference was calculated for CTA ADG over V/Q scan integrating ANOVA p value set at 0.05.

Results

The sensitivity for CT was 86.0% (95% CI: 80.2%, 92.1%) and specificity of 93.7% (95% CI: 91.1%, 96.3%). The V/Q scan yielded a sensitivity of 96% (95% CI: 95%, 97%) and a specificity of 97% (95% CI: 96%, 98%). Bayes nomogram results for CTA were low risk and yielded a posttest probability of 71.1%, an ADG of 56.1%, and an RDG of 374%, moderate-risk posttest probability was 85.1%, an ADG of 56.1%, and an RDG of 193.4%, and high-risk posttest probability was 95.2%, an ADG of 36.2%, and an RDG of 61.35%. The comparative gain difference for low-risk population was 46.1%; in moderate-risk 41.6%; and in high-risk a 22.1% superiority. ANOVA analysis for LR+ and LR? showed no significant difference (p = 0.8745, p = 0.9841 respectively).

Conclusions

This Bayesian model demonstrated a superiority of CTA when compared to V/Q scan for the diagnosis of pulmonary embolism. Low-risk patients are recognized to have a superior overall comparative gain favoring CTA.

     

Bayesian comparative assessment of diagnostic accuracy of low-dose CT scan and ultrasonography in the diagnosis of urolithiasis after the application of the STONE score

Objective

The objective of our study was to assess the diagnostic quality of low-dose computed tomography (CT) when compared to ultrasound (US) in diagnosis of urolithiasis using STONE score as a predictor of pre-test probability and the Bayesian statistical model to calculate post-test probabilities (POST) for both diagnostic tests.

Methods

STONE score was used to form risk groups to obtain pre-test probabilities. Likelihood ratios (LR) were calculated from external data for low-dose CT and US. POST were obtained using pre-test probabilities and likelihood ratios with Bayesian nomogram. Absolute (ADG) and relative (RDG) gains in diagnostic value were calculated.

Results

Calculated +LR for US was 12 and ?LR was 0.32; for CT, +LR was 19 and ?LR 0.04. +LR and low STONE for US yielded POST 57% and RDG 470%; intermediate STONE POST 92% and RDG 84%; and high STONE POST 99% and RDG 10%. ?LR and low STONE for US POST 3% and RDG ?70%; intermediate POST 24% and RDG ?52%; and high STONE POST 74% and RDG ?17.7%. +LR and low STONE for CT POST 68% and RDG 580%; moderate STONE POST 95% and RDG 90%; and high STONE POST 99% and RDG 10%. ?LR and low STONE for CT POST 0% and RDG ?100%; intermediate POST 4% and RDG ?92%; and high STONE POST 26% and RDG ?71.1%. ANOVA calculations comparing CT vs US for +LR showed no statistical significance (P value = 0.9893; LR? P value = 0.5488).

Conclusion

Bayesian statistical analysis demonstrated slight superiority of CT scan over US on STONE score low- and moderate-risk stratified subtypes, whereas no significant advantage was seen when evaluating high-probability patients.